In this work we report on the remarkable voltammetric features of graphite electrodes, which were modified with mercaptoundecanoic acid derivatized gold nanoparticles (Au-MUA NPs) by simple adsorption from basic aqueous solution. Atomic force microscopy measurements proved a fairly uniform adsorption of the nanoparticles in the form of clusters, and consecutive island formation. The electrochemical features of the modified electrodes were probed by cyclic voltammetry, while using various redox probes in several different setups. The catalytic effects of the adsorbed clusters on the graphite electrodes proved to be highly reproducible, time dependent, and of nonselective nature. The main advantages of the proposed methodology are seen by the simplicity of the modification procedure, the stability of the self-assembled gold nanoparticle film, their applicability in various voltammetric scenarios, and the potential employment of the Au-MUA NP modified electrodes as sensors for various systems.

Chemical carcinogenicity is of primary interest, because it drives much of the current regulatory actions regarding new and existing chemicals, and its conventional experimental test takes around three years to design, conduct, and interpret as well as the costs of hundreds of millions of dollars, millions of skilled personnel hours, and several animal lives. Both academia and private companies are actively trying to develop alternative methods, such as QSAR models. This paper reports a QSAR study for predicting carcinogenic potency of nitrocompounds bioassayed in female rats. Several different theoretical molecular descriptors, calculated only on the basis of knowledge of the molecular structure and an efficient variable selection procedure, such as Genetic Algorithm, led to models with satisfactory predictive ability. But the best-final QSAR model is based on the GEometry, Topology, and Atom-Weights AssemblY (GETAWAY) descriptors capturing a reasonable interpretation. In fact, structural features such as molecular shape-linear, branched, cyclic, and polycyclic-and bond length are some of the key factors flagging the carcinogenicity of this set of nitrocompounds. This QSAR model, after removal of one identified nitrocompound outlier, is able to explain around 86% of the variance in the experimental activity and manifest good predictive ability as indicated by the higher q(2)s of cross- and external-validations, which demonstrate the practical value of the final QSAR model for screening and priority testing. This model can be applied to nitrochemicals different from the studied nitrocompounds (even those not yet synthesized) as it is based on theoretical molecular descriptors that might be easily and rapidly calculated.

Motivation. Antagonism of human gonadotropin-hormon-releasing receptor (hGnRH-R) using peptide antagonists is a way to treat a variety of sex-hormone-dependent diseases. However, low bioavailability of these peptide antagonists intensifies the need for some orally active small molecules, which may act as hGnRH-R antagonists. To find more active compounds, QSAR study was performed on some orally active substituted uracil derivatives. Method. A QSAR study of 32 derivatives of orally active substituted uracils was done using topological and quantum chemical descriptors. Correlation and multiple regression analyses were performed to develop QSAR models. Results. Results show that the importance of ETSA and RTSA indices of two particular atoms. ETSA index of the atom number 17 and RTSA index of the atom number 19 are important because these atoms may involve in electronic interaction and van der Waals interaction with the receptor respectively. The study also shows the importance of average atomic charges of atom numbers 1, 2, 3, 4, 5, 6 and 9. It suggests the importance of approximate surface area in biological activity. Frontier electron density for electrophilic attack at atom number 24 is also found to be useful for the small molecular antagonism towards the receptor. Conclusions. The pharmacophoric requirement for the substituted uracil derivatives for their human GnRH-R antagonism is illustrated optimally by two tetravariate QSAR models. These models show that compounds with reduced surface area, higher atomic charge and lower electrophilic attack at the atom number 24 may have an increased binding affinity towards hGnRH receptor. © 2008 BioChem Press.

Numerical parameters of the molecular networks, also referred as Topological Indices or Connectivity Indices (CIs), have been used in Bioorganic and Medicinal Chemistry to find Quantitative Structure-Activity, Property or Toxicity Relationship (QSAR, QSPR and QSTR) models. QSPR models generally use CIs as inputs to predict the biological activity of compounds. However, the literature does not evidence a great effort to find QSAR-like models for other biologically and chemically relevant systems. For instance, blood proteome constitutes a protein-rich information reservoir, since the serum proteome Mass Spectra (MS) represents a potential information source for the early detection of Biomarkers for diseases and/or drug-induced toxicities. The concept of mass spectrum network (MS network) for a single protein is already well-known. However, there are no reported results on the use of CIs for a MS network of a whole proteome to explore MS patterns. In this work, we introduced for the first time a novel network representation and the CIs for the MS of blood proteome samples. The new network bases on Randic's Spiral network have been previously introduced for protein sequences. The new MS CIs, called here Spiral Markov Connectivity (SMCk) of the MS Spiral graph can be calculated with the software MARCH-INSIDE, combining network and Markov model theory. The SMCk values could be used to seek QSAR-like models, called in this work Quantitative Proteome-Property Relationships (QPPRs). We calculate the SMCk values for 62 blood samples and fit a QPPR model by discriminating proteome MS, typical of individuals susceptible to suffer drug-induced cardiotoxicity from control samples. The accuracy, sensitivity, and specificity values of the QPPR model were between 73.08% and 87.5% in training and validation series. This work points to QPPR models as a powerful tool for MS detection of biomarkers in proteomics.

Chemical carcinogenicity is of primary interest because it drives much of the current regulatory actions regarding new and existing chemicals and conventional experimental tests take around 3 years to design, conduct, and interpret in addition to costing hundreds of millions of dollars, millions of skilled personnel hours, and millions of animal lives. Thus, theoretical approaches such as the one proposed here, quantitative structure-activity relationship (QSAR), are increasingly used for assessing the risks of environmental chemicals, since they can markedly reduce costs, avoid animal testing, and speed up policy decisions. This paper reports a QSAR study based on the TOPological Substructural MOlecular DEsign (TOPS-MODE) approach, aimed at predicting the rodent carcinogenicity of a set of nitroso compounds selected from the Carcinogenic Potency Data Base (CPDB). The set comprises 26 nitroso compounds, divided into N-nitrosoureas, N-nitrosamines, and C-nitroso compounds, which have been bioassayed in female rats using gavage as a route of administration. Here, we are especially concerned in discerning the role of structural parameters on the carcinogenic activity of this family of compounds. First, the regression model derived, upon removal of two identified nitrosamine outliers, is able to account for more than 86% of the variance in the experimental activity. Second, TOPS-MODE afforded the bond contributions (expressed as fragment contributions to the carcinogenic activity) that can be interpreted and provided tools for better understanding of the mechanisms of carcinogenesis. Finally and, most importantly, we demonstrate the potential use of this approach toward the recognition of structural alerts for carcinogenicity predictions.

In this work, Quantitative Structure-Activity Relationship (QSAR) modelling was used as a tool for predicting the carcinogenic potency of a set of 39 nitroso-compounds, which have been bioassayed in male rats by using the oral route of administration. The optimum QSAR model provided evidence of good fit and performance of predicitivity from training set. It was able to account for about 84% of the variance in the experimental activity and exhibited high values of the determination coefficients Of Cross validations, leave one out and bootstrapping (q(LOO)(2) = 78.53 and q(Boot)(2) = 74.97). Such a model was based on spectral moments weighted with Gasteiger-Marsilli atomic charges, polarizability and hydrophobicity, as well as with Abraham indexes, specifically the summation solute hydrogen bond basicity and the combined dipolarity/polarizability. This is the first study to have explored the possibility of combining Abraham solute descriptors with spectral moments. A reasonable interpretation of these molecular descriptors from a toxicological point of view was achieved by means of taking into account bond contributions. The set of relationships so derived revealed the importance of the length of the alkyl chains for determining carcinogenic potential of the chemicals analysed, and were able to explain the difference between mono-substituted and di-substituted nitrosoureas as well as to discriminate between isomeric structures with hydroxyl-alkyl and alkyl substituents in different positions. Moreover, they allowed the recognition of structural alerts in classical structures of two potent nitrosamines, consistent with their biotransformation. These results indicate that this new approach has the potential for improving carcinogenicity predictions based on the identification of structural alerts.

Fura-2 is one of the most commonly used fluorescent dyes to analyze the cytosolic Ca(2+) concentration ([Ca(2+)](i)) of living cells. Fura-2-dependent measurements of [Ca(2+)](i) are susceptible to changes of pH, reactive oxygen species concentration and membrane potential. Fura-2 is often loaded over the lipophilic cell membrane into the cytosol of a cell in its esterified form (Fura-2/AM) which is then cleaved by endogenous esterases. We have analyzed the electrochemical properties of Fura-2/AM and Fura-2 salt by cyclic voltammetry ("three-phase" and "thin-film" electrode methods). Using Fura-2/AM as a redox facilitator, we were able to mimic the transport of various ions across a lipophilic barrier. We show that Fura-2/AM in this biomimetic set-up can be reversibly oxidized in a single electrochemical step. Its redox reaction was highly proton sensitive in buffers with pH <= 6. At physiological pH of around 7.0, the oxidation of Fura-2/AM was coupled to an uptake of mono-anions across the liquid-liquid interface. The voltage-dependence of the redox cycle was sensitive to the free Ca(2+) concentration, either after de-esterification of Fura-2/AM, or when Fura-2 salt was used. The complex between Fura-2 and Ca(2+) ions is ionic (complexation occurs via the dissociated negative groups of Fura forms), while the redox transformations in Fura-2 occurs at the nitrogen atoms of the amino groups. Our results suggest that redox transformations of the Fura-2 forms do not affect the binding ability toward Ca(2+) ions and thus do not interfere with [Ca(2+)](i) measurements.

The Quantitative Structure-Property Relationships (QSPRs) based on Graph OF Network Theory are important for predicting the properties of polymeric systems. In the three previous papers of this series (Polymer 45 (2004) 3845-3853; Polymer 46 (2005) 2791-2798: and Polymer 46 (2005) 6461-6473) we focused on the uses of molecular graph parameters called topological indices (TIs) to link the structure of polymers with their biological properties. However, there has been little effort to extend these TIs to the Study of complex mixtures of artificial polymers OF biopolymers such as nucleic acids and proteins. In this sense, Blood Proteome (BP) is one of the most important and complex mixtures containing protein polymers. For instance, outcomes obtained by Mass Spectrometry (MS) analysis of BP are very useful for the early detection of diseases and drug-induced toxicities. Here, we use two Spiral and Star Network representations of the MS outcomes and defined a new type of TIs. The new TIs introduced here are the spectral moments (pi(k)) of the stochastic matrix associated to the Spiral graph and describe non-linear relationships between the different regions of the MS characteristic of BR We used the MARCH-INSIDE approach to calculate the pi(k)(SN) of different BP samples and S2SNet to determine several Star graph TIs. In the second step, we develop the corresponding Quantitative Proteome-Property Relationship (QPPR) models using the Linear Discriminant Analysis (LDA). QPPRs are the analogues of QSPRs in the case of complex biopolymer mixtures. Specifically, the new QPPRs derived here may be used to detect drug-induced cardiac toxicities from BP samples. Different Machine Learning classification algorithms were used to fit the QPPRs based on pi(k)(SN), showing J48 decision tree classifier to have the best performance. These results suggest that the present approach Captures important features of the complex biopolymers mixtures and opens new opportunities to the application of the idea supporting classic QSPRs in polymer sciences.